Collecting data and sampling

Maths: Applications and Interpretation · Statistics and probability. The idea in plain words, the rules to remember, the mistake to avoid and a worked example, written and checked by Mega Lecture.

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The idea

The population is the whole group you want to know about. A sample is the part of it that you actually measure. A good sample is chosen so that it represents the population; if some members are more likely to be chosen than others, the results are biased.

Data that is counted, such as the number of goals, is discrete. Data that is measured and can take any value in a range, such as mass or time, is continuous.

Before using data, ask where it came from and whether it can be trusted. Also look for outliers, which are values far from the rest. An outlier may be an error or a real unusual value, so check it before you remove it.

Rules to remember

Common mistake

Students multiply the quartiles by 1.5 or measure 1.5 × IQR from the median. The boundaries are measured from the quartiles: Q1 − 1.5 × IQR and Q3 + 1.5 × IQR.

Worked example

A data set has Q1 = 34 and Q3 = 46. Decide whether the values 15 and 63 are outliers.

  1. IQR = 46 − 34 = 12, so 1.5 × IQR = 18.
  2. Lower boundary = 34 − 18 = 16; upper boundary = 46 + 18 = 64.
  3. 15 is less than 16, so it is an outlier. 63 is not greater than 64, so it is not an outlier.

Answer: 15 is an outlier; 63 is not.

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